Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier

Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.

Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.

Implement a sliding-window technique and use your trained classifier to search for vehicles in images.

Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.

Design, train and validate a model that predicts a steering angle from image data

Use the model to drive the vehicle autonomously around the first track in the simulator. The vehicle should remain on the road for an entire loop around the track.

Summarize the results with a written report

The project uses a convolutional neural network to mimic driving behavior and successfully guide an autonomous vehicle around a simulated track by predicting the steering angle. The project requires collecting appropriate data to train the model.

The writeup describes the approach taken to data collection, model architecture selection, and model training.